AI-ML Intern (Full Time -Remote) at SarvM.AI

At SarvM AI, we're improving our award-winning Agentic AI solutions that act autonomously, reason deeply, and execute complex tasks with minimal supervision. We’re looking for passionate minds to join our mission and shape the future of next-gen AI agents, LLMs, and prompt-driven intelligence.

• Design, develop, and fine-tune prompting strategies for agent behavior, task orchestration, and complex multi-agent workflows.

• Build LLM-centric pipelines that include fine-tuning, RLHF, post-training optimization, and persona alignment for domain-specific agents.
• Collaborate with cross-functional teams to translate business objectives into intelligent agent workflows using data + context-driven prompting.
• Lead the full ML pipeline: from data acquisition, cleaning, and preprocessing to training, validation, and production deployment.
• Prototype and deploy autonomous AI agents powered by internal knowledge bases, APIs, and third-party tool integration.
• Develop evaluation benchmarks to assess performance, bias, and robustness of LLMs and agents in real-world conditions.
• Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress
• Managing available resources such as hardware, data, and personnel so that deadlines are met
• Analyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability
• Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world
• Verifying data quality, and/or ensuring it via data cleaning
• Supervising the data acquisition process if more data is needed
• Finding available datasets online that could be used for training
• Defining validation strategies
• Defining the preprocessing or feature engineering to be done on a given dataset
• Defining data augmentation pipelines
• Training models and tuning their hyper parameters
• Analyzing the errors of the model and designing strategies to overcome them
• Deploying models to production

Skills We Value:

• Experience with prompt engineering, LLMs, LangChain, or similar agent frameworks.
• Familiarity with prompting, Chain-of-thought (CoT) prompting, RLHF, LoRA, fine-tuning techniques, and knowledge distillation.
• Python mastery, along with libraries like scikit-learn, pandas, PyTorch or TensorFlow.
• Solid understanding of validation, data augmentation, hyperparameter tuning, and model performance metrics.
• Comfortable visualizing, manipulating, and debugging large-scale datasets.
• Hands-on experience in deploying ML models and agent-based apps to production environments.
• Some experience with a deep learning framework such as PyTorch, TensorFlow.
• Proficiency with Python and basic libraries for machine learning such as scikit-learn and pandas
• Expertise in visualizing and manipulating big datasets
• Proficiency with OpenCV
• Familiarity with Linux
• Ability to select hardware to run an ML model with the required latency

Bonus:

• Understanding of Toolformer/Function Calling, multi-agent coordination, and AI safety alignment
• Exposure to retrieval-augmented generation (RAG) or knowledge-grounded systems